In a process control industry, Advanced Process Control (APC) is employed to reduce operating costs, achieve high productivity, maintain quality, and for other similar reasons. APC allows transition from present operating schema to an improved and more productive operating schema of the process control industry, and also accommodate operating and design constraints of the process involved in the process control industry.
Known multivariable APCs can implement an advanced multivariable control scheme called Model Predictive Control (MPC) in Multivariable Predictive Controllers. MPCs use a mathematical model of the process involved in the process plant, in order to predict the future dynamic behavior of the process and accordingly provide optimal manipulated variables for the process and operation of the plant thereof. From this, it can be understood that accuracy of the model is a key element in effective and successful implementation of MPC.
Plant dynamics changes are resulting in a mismatch between the model and the plant, termed as Model Plant Mismatch (MPM). MPM leads to inaccurate predictions of the plant dynamics. Using APC having a model impacted by the MPM can degrade the MPC and overall control performance thereof, which can also alter the product quality and causes economic losses.
It becomes important to update the model upon detection of poor performance of the controller, in order to eliminate the performance degradation of the controller. Poor performance of the controller can be detected by well-established MPC performance monitoring. For instance, a simple approach could be to analyse the prediction errors, which being the difference between the model predictions and true outputs. After the detection of poor performance of the controller, cause for the same, such as poor model (e.g., MPM), unmeasured disturbance, and constraint saturation etc. can be identified and diagnosed using established diagnosis techniques.
In know systems upon identification or detection of a poor model, MPM is diagnosed by and after re-identification of the model. Re-identification of the model involves designing the perturbation signal, deciding and/or considering the operating conditions of the plant during perturbation, choosing an appropriate model and estimating model parameters. This can call for a high degree of expertise and can be time consuming. Also, it can involve a longer perturbation period, by which a large amount or number of product with low quality, usually termed as off spec product are produced during the perturbation period.